When a transmission is made in a multipath environment, the propagating channel introduces distortions in the transmitted signal which degrade the signal quality at the receiver. In many wireless communications systems, knowledge of the channel state is required to properly demodulate the transmission. Thus, a channel estimate is performed at the receiver and is subsequently used to demodulate data.
Quadrature amplitude modulation (QAM) is a method of combining two amplitude-modulated (AM) signals into a single channel, thereby doubling the effective bandwidth. QAM is used with pulse amplitude modulation (PAM) in digital systems, especially in wireless applications. In a QAM signal, there are two carriers, each having the same frequency but differing in phase by ninety degrees, (i.e., one quarter of a cycle, from which the term quadrature arises). One signal is called the real or in-phase (I) signal and the other is called the imaginary or quadrature (Q) signal. Mathematically, one of the signals can be represented by a sine wave, and the other by a cosine wave. The two modulated carriers are combined at the source for transmission. At the destination, the carriers are separated, the data is extracted from each, and then the data is combined into the original modulating information.
In digital applications, the modulating signal is generally quantized in both its in-phase and ninety degree components. The set of possible combinations of amplitudes, as shown on an x-y plot, is a pattern of dots known as a QAM constellation. This constellation, and therefore the number of bits which can be transmitted at once, can be increased for higher bit rates and faster throughput, or decreased for more reliable transmission with fewer bit errors. The number of “dots” in the constellation is given as a number before the QAM, and is often an integer power of two, i.e., from 21 (2QAM) to 212 (4096QAM).
In many wireless systems, such as frequency division duplex (FDD), time division duplex (TDD), and IEEE 802.11 systems, the channel estimate is performed based on a known transmission, i.e., a pilot signal. However, the channel state changes over a period of time and therefore the channel estimate may no longer be an accurate estimate of the channel during much of the transmission. The effect of the channel drift, in part, can be seen in the constellation diagram of a packet of received symbols as distinctly non-Gaussian noise or distortion about the constellation points.
One method to compensate for channel drift is to perform channel estimates at a higher rate. When the pilot signal is time multiplexed with the data, this may be difficult. When the pilot signal is continuously transmitted, channel estimates can be performed at an arbitrary rate, but may pose an unacceptable computational burden or processing delay.
Adaptive receivers, such as normalized least mean squared (NLMS) equalizers, also suffer degradation that can be seen in the constellation diagram even when a continuous pilot signal is present. In this case, it is not the lack of current channel estimation that causes the distortion, but rather it is due to the receiver remaining in a tracking state and thus never converges. The effect is equivalent to the above description of receivers that have channel estimates that become increasingly unreliable after they are created, i.e., the adaptive receiver has an implied channel estimate that is always delayed and therefore is not completely reflective of the current channel conditions.